






'''simple docstring'''







import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class   __SCREAMING_SNAKE_CASE       (__A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : List[str]      			=     MgpstrTokenizer
  _a  : str      			=     False
  _a  : Optional[Any]      			=     {}
  _a  : Optional[Any]      			=     False







  def    _a       (			self	):






         """simple docstring"""


         super().setUp()

         # fmt: off
         a_						     =			['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
         # fmt: on
         a_						     =			dict(zip(UpperCamelCase__   ,	range(len(UpperCamelCase__	)	)	)	)

         a_						     =			os.path.join(self.tmpdirname   ,	VOCAB_FILES_NAMES['vocab_file']	)
         with open(self.vocab_file   ,	'w'   ,	encoding='utf-8'	) as fp:
                fp.write(json.dumps(UpperCamelCase__	) + '\n'	)







  def    _a       (			self   ,	**UpperCamelCase__	):






         """simple docstring"""


         return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			'tester'
         a_						     =			'tester'
         return input_text, output_text







  @unittest.skip('MGP-STR always lower cases letters.'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.get_tokenizers(do_lower_case=UpperCamelCase__	)
         for tokenizer in tokenizers:
                with self.subTest(f'{tokenizer.__class__.__name__}'	):
                       a_						     =			'[SPECIAL_TOKEN]'

                       tokenizer.add_special_tokens({'cls_token': special_token}	)
                       a_						     =			tokenizer.encode([special_token]   ,	add_special_tokens=UpperCamelCase__	)
                       self.assertEqual(len(UpperCamelCase__	)   ,	1	)

                       a_						     =			tokenizer.decode(UpperCamelCase__   ,	skip_special_tokens=UpperCamelCase__	)
                       self.assertTrue(special_token not in decoded	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.get_tokenizers()
         for tokenizer in tokenizers:
                with self.subTest(f'{tokenizer.__class__.__name__}'	):
                       a_     ,	a_						     =			self.get_input_output_texts(UpperCamelCase__	)

                       a_						     =			tokenizer.tokenize(UpperCamelCase__	)
                       a_						     =			tokenizer.convert_tokens_to_ids(UpperCamelCase__	)
                       a_						     =			tokenizer.encode(UpperCamelCase__   ,	add_special_tokens=UpperCamelCase__	)
                       self.assertListEqual(UpperCamelCase__   ,	UpperCamelCase__	)

                       a_						     =			tokenizer.convert_ids_to_tokens(UpperCamelCase__	)
                       self.assertNotEqual(len(UpperCamelCase__	)   ,	0	)
                       a_						     =			tokenizer.decode(UpperCamelCase__	)
                       self.assertIsInstance(UpperCamelCase__   ,	UpperCamelCase__	)

                       self.assertEqual(text_a.replace(' '   ,	''	)   ,	UpperCamelCase__	)







  @unittest.skip('MGP-STR tokenizer only handles one sequence.'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  @unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'	)
  def    _a       (			self	):






         """simple docstring"""


         pass












'''simple docstring'''







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def       __UpperCamelCase					(      *lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       if not isinstance(lowercase_   ,       lowercase_   ):
              a_						     =			list(lowercase_   )
       for i in range(len(lowercase_   )   ):
              a_						     =			None
       gc.collect()
       if is_xpu_available():
              torch.xpu.empty_cache()
       elif is_npu_available():
              torch.npu.empty_cache()
       else:
              torch.cuda.empty_cache()
       return objects


def       __UpperCamelCase					(      lowercase_       :  Exception   ):
       """simple docstring"""




       a_						     =			[
           'CUDA out of memory.',  # CUDA OOM
           'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.',  # CUDNN SNAFU
           'DefaultCPUAllocator: can\'t allocate memory',  # CPU OOM
       ]
       if isinstance(lowercase_   ,       lowercase_   ) and len(exception.args   ) == 1:
              return any(err in exception.args[0] for err in _statements   )
       return False


def       __UpperCamelCase					(      lowercase_       :  callable = None   ,       lowercase_       :  int = 128   ):
       """simple docstring"""




       if function is None:
              return functools.partial(lowercase_   ,       starting_batch_size=lowercase_   )

       a_						     =			starting_batch_size

       def decorator(*lowercase_       :  str   ,       **lowercase_       :  Dict   ):
              nonlocal batch_size
              gc.collect()
              if is_xpu_available():
                     torch.xpu.empty_cache()
              elif is_npu_available():
                     torch.npu.empty_cache()
              else:
                     torch.cuda.empty_cache()
              a_						     =			list(inspect.signature(lowercase_   ).parameters.keys()   )
              # Guard against user error
              if len(lowercase_   ) < (len(lowercase_   ) + 1):
                     a_						     =			', '.join([F'{arg}={value}' for arg, value in zip(params[1:]   ,       args[1:]   )]   )
                     raise TypeError(
                         F'Batch size was passed into `{function.__name__}` as the first argument when called.'
                         F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'   )
              while True:
                     if batch_size == 0:
                            raise RuntimeError('No executable batch size found, reached zero.'   )
                     try:
                            return function(lowercase_   ,       *lowercase_   ,       **lowercase_   )
                     except Exception as e:
                            if should_reduce_batch_size(lowercase_   ):
                                   gc.collect()
                                   if is_xpu_available():
                                          torch.xpu.empty_cache()
                                   elif is_npu_available():
                                          torch.npu.empty_cache()
                                   else:
                                          torch.cuda.empty_cache()
                                   batch_size //= 2
                            else:
                                   raise

       return decorator




